'''simple docstring'''






from __future__ import annotations

import unittest

from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
   import tensorflow as tf

   from transformers import (
       TFDebertaVaForMaskedLM,
       TFDebertaVaForQuestionAnswering,
       TFDebertaVaForSequenceClassification,
       TFDebertaVaForTokenClassification,
       TFDebertaVaModel,
   )







class lowercase_       :







   """simple docstring"""

   def __init__(      self      :			str, UpperCamelCase__      :			Optional[int], UpperCamelCase__      :			int=13, UpperCamelCase__      :			int=7, UpperCamelCase__      :			List[str]=True, UpperCamelCase__      :			Union[str, Any]=True, UpperCamelCase__      :			str=True, UpperCamelCase__      :			Optional[Any]=True, UpperCamelCase__      :			Dict=99, UpperCamelCase__      :			Any=32, UpperCamelCase__      :			str=2, UpperCamelCase__      :			Union[str, Any]=4, UpperCamelCase__      :			Any=37, UpperCamelCase__      :			List[str]="gelu", UpperCamelCase__      :			Any=0.1, UpperCamelCase__      :			int=0.1, UpperCamelCase__      :			List[Any]=5_12, UpperCamelCase__      :			List[str]=16, UpperCamelCase__      :			int=2, UpperCamelCase__      :			Union[str, Any]=0.02, UpperCamelCase__      :			Optional[int]=False, UpperCamelCase__      :			List[Any]=True, UpperCamelCase__      :			List[str]="None", UpperCamelCase__      :			List[Any]=3, UpperCamelCase__      :			Tuple=4, UpperCamelCase__      :			Dict=None, )  ->			Tuple:
        _A    		=  parent
        _A    		=  batch_size
        _A    		=  seq_length
        _A    		=  is_training
        _A    		=  use_input_mask
        _A    		=  use_token_type_ids
        _A    		=  use_labels
        _A    		=  vocab_size
        _A    		=  hidden_size
        _A    		=  num_hidden_layers
        _A    		=  num_attention_heads
        _A    		=  intermediate_size
        _A    		=  hidden_act
        _A    		=  hidden_dropout_prob
        _A    		=  attention_probs_dropout_prob
        _A    		=  max_position_embeddings
        _A    		=  type_vocab_size
        _A    		=  type_sequence_label_size
        _A    		=  initializer_range
        _A    		=  num_labels
        _A    		=  num_choices
        _A    		=  relative_attention
        _A    		=  position_biased_input
        _A    		=  pos_att_type
        _A    		=  scope
   def 	__UpperCAmelCase   (      self      :			int     )  ->			int:
        _A    		=  ids_tensor([self.batch_size, self.seq_length], self.vocab_size     )

        _A    		=  None
        if self.use_input_mask:
             _A    		=  random_attention_mask([self.batch_size, self.seq_length]     )

        _A    		=  None
        if self.use_token_type_ids:
             _A    		=  ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size     )

        _A    		=  None
        _A    		=  None
        _A    		=  None
        if self.use_labels:
             _A    		=  ids_tensor([self.batch_size], self.type_sequence_label_size     )
             _A    		=  ids_tensor([self.batch_size, self.seq_length], self.num_labels     )

        _A    		=  DebertaVaConfig(
            vocab_size=self.vocab_size, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, hidden_act=self.hidden_act, hidden_dropout_prob=self.hidden_dropout_prob, attention_probs_dropout_prob=self.attention_probs_dropout_prob, max_position_embeddings=self.max_position_embeddings, type_vocab_size=self.type_vocab_size, relative_attention=self.relative_attention, position_biased_input=self.position_biased_input, initializer_range=self.initializer_range, return_dict=UpperCamelCase__, )

        return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
   def 	__UpperCAmelCase   (      self      :			Tuple, UpperCamelCase__      :			Any, UpperCamelCase__      :			Dict, UpperCamelCase__      :			Tuple, UpperCamelCase__      :			Any, UpperCamelCase__      :			Optional[int], UpperCamelCase__      :			int, UpperCamelCase__      :			Optional[int]     )  ->			str:
        _A    		=  TFDebertaVaModel(config=UpperCamelCase__     )
        _A    		=  {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}

        _A    		=  [input_ids, input_mask]
        _A    		=  model(UpperCamelCase__     )

        _A    		=  model(UpperCamelCase__     )

        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)     )
   def 	__UpperCAmelCase   (      self      :			Any, UpperCamelCase__      :			Union[str, Any], UpperCamelCase__      :			List[Any], UpperCamelCase__      :			Any, UpperCamelCase__      :			List[Any], UpperCamelCase__      :			Optional[int], UpperCamelCase__      :			int, UpperCamelCase__      :			List[str]     )  ->			Union[str, Any]:
        _A    		=  TFDebertaVaForMaskedLM(config=UpperCamelCase__     )
        _A    		=  {
            'input_ids': input_ids,
            'attention_mask': input_mask,
            'token_type_ids': token_type_ids,
        }
        _A    		=  model(UpperCamelCase__     )
        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size)     )
   def 	__UpperCAmelCase   (      self      :			Optional[Any], UpperCamelCase__      :			int, UpperCamelCase__      :			str, UpperCamelCase__      :			Any, UpperCamelCase__      :			Dict, UpperCamelCase__      :			List[str], UpperCamelCase__      :			Tuple, UpperCamelCase__      :			str     )  ->			List[Any]:
        _A    		=  self.num_labels
        _A    		=  TFDebertaVaForSequenceClassification(config=UpperCamelCase__     )
        _A    		=  {
            'input_ids': input_ids,
            'attention_mask': input_mask,
            'token_type_ids': token_type_ids,
        }

        _A    		=  model(UpperCamelCase__     )
        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels)     )
   def 	__UpperCAmelCase   (      self      :			Tuple, UpperCamelCase__      :			Tuple, UpperCamelCase__      :			List[str], UpperCamelCase__      :			int, UpperCamelCase__      :			Optional[Any], UpperCamelCase__      :			List[Any], UpperCamelCase__      :			int, UpperCamelCase__      :			List[str]     )  ->			Optional[Any]:
        _A    		=  self.num_labels
        _A    		=  TFDebertaVaForTokenClassification(config=UpperCamelCase__     )
        _A    		=  {
            'input_ids': input_ids,
            'attention_mask': input_mask,
            'token_type_ids': token_type_ids,
        }
        _A    		=  model(UpperCamelCase__     )
        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels)     )
   def 	__UpperCAmelCase   (      self      :			Union[str, Any], UpperCamelCase__      :			Tuple, UpperCamelCase__      :			Union[str, Any], UpperCamelCase__      :			int, UpperCamelCase__      :			Tuple, UpperCamelCase__      :			str, UpperCamelCase__      :			Any, UpperCamelCase__      :			Union[str, Any]     )  ->			Any:
        _A    		=  TFDebertaVaForQuestionAnswering(config=UpperCamelCase__     )
        _A    		=  {
            'input_ids': input_ids,
            'attention_mask': input_mask,
            'token_type_ids': token_type_ids,
        }

        _A    		=  model(UpperCamelCase__     )
        self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length)     )
        self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length)     )

   def 	__UpperCAmelCase   (      self      :			Tuple     )  ->			str:
        _A    		=  self.prepare_config_and_inputs()
        (
            (
            _A
        )		,       (
            _A
        )		,       (
            _A
        )		,       (
            _A
        )		,       (
            _A
        )		,       (
            _A
        )		,       (
            _A
        )		,       
        )    		=  config_and_inputs
        _A    		=  {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
        return config, inputs_dict







@require_tf
class lowercase_       (		_UpperCamelCase    ,    _UpperCamelCase    ,    unittest.TestCase			):







   """simple docstring"""

   __lowerCAmelCase         =   (
       (
           TFDebertaVaModel,
           TFDebertaVaForMaskedLM,
           TFDebertaVaForQuestionAnswering,
           TFDebertaVaForSequenceClassification,
           TFDebertaVaForTokenClassification,
       )
       if is_tf_available()
       else ()
   )
   __lowerCAmelCase         =   (
       {
           "feature-extraction": TFDebertaVaModel,
           "fill-mask": TFDebertaVaForMaskedLM,
           "question-answering": TFDebertaVaForQuestionAnswering,
           "text-classification": TFDebertaVaForSequenceClassification,
           "token-classification": TFDebertaVaForTokenClassification,
           "zero-shot": TFDebertaVaForSequenceClassification,
       }
       if is_tf_available()
       else {}
   )

   __lowerCAmelCase         =   False
   __lowerCAmelCase         =   False
   def 	__UpperCAmelCase   (      self      :			Dict     )  ->			Dict:
        _A    		=  TFDebertaVaModelTester(self     )
        _A    		=  ConfigTester(self, config_class=UpperCamelCase__, hidden_size=37     )
   def 	__UpperCAmelCase   (      self      :			Union[str, Any]     )  ->			Optional[Any]:
        self.config_tester.run_common_tests()
   def 	__UpperCAmelCase   (      self      :			Dict     )  ->			List[Any]:
        _A    		=  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*UpperCamelCase__     )
   def 	__UpperCAmelCase   (      self      :			Optional[Any]     )  ->			Any:
        _A    		=  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_lm(*UpperCamelCase__     )
   def 	__UpperCAmelCase   (      self      :			Union[str, Any]     )  ->			List[Any]:
        _A    		=  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(*UpperCamelCase__     )
   def 	__UpperCAmelCase   (      self      :			Dict     )  ->			Optional[int]:
        _A    		=  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_sequence_classification(*UpperCamelCase__     )
   def 	__UpperCAmelCase   (      self      :			Any     )  ->			Optional[int]:
        _A    		=  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(*UpperCamelCase__     )

   @slow
   def 	__UpperCAmelCase   (      self      :			Union[str, Any]     )  ->			Union[str, Any]:
        _A    		=  TFDebertaVaModel.from_pretrained('kamalkraj/deberta-v2-xlarge'     )
        self.assertIsNotNone(UpperCamelCase__     )







@require_tf
class lowercase_       (		unittest.TestCase			):







   """simple docstring"""

   @unittest.skip(reason='Model not available yet'     )
   def 	__UpperCAmelCase   (      self      :			int     )  ->			str:
        pass

   @slow
   def 	__UpperCAmelCase   (      self      :			Union[str, Any]     )  ->			int:
        _A    		=  TFDebertaVaModel.from_pretrained('kamalkraj/deberta-v2-xlarge'     )
        _A    		=  tf.constant([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]     )
        _A    		=  tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]     )
        _A    		=  model(UpperCamelCase__, attention_mask=UpperCamelCase__     )[0]

        _A    		=  tf.constant(
            [[[0.2_356, 0.1_948, 0.0_369], [-0.1_063, 0.3_586, -0.5_152], [-0.6_399, -0.0_259, -0.2_525]]]     )
        tf.debugging.assert_near(output[:, 1:4, 1:4], UpperCamelCase__, atol=1e-4     )




'''simple docstring'''




from manim import *




class        a__ (  UpperCAmelCase__					):






     def   SCREAMING_SNAKE_CASE__  (					self						:						List[Any]       ):





         """simple docstring"""



         __lowerCamelCase            =    Rectangle(height=0.5    ,							width=0.5       )
         __lowerCamelCase            =    Rectangle(height=0.46    ,							width=0.46       ).set_stroke(width=0       )

         __lowerCamelCase            =    Rectangle(height=0.25    ,							width=0.25       )

         __lowerCamelCase            =    [mem.copy() for i in range(6       )]
         __lowerCamelCase            =    [mem.copy() for i in range(6       )]
         __lowerCamelCase            =    VGroup(*a       ).arrange(a    ,							buff=0       )
         __lowerCamelCase            =    VGroup(*a       ).arrange(a    ,							buff=0       )
         __lowerCamelCase            =    VGroup(a    ,							a       ).arrange(a    ,							buff=0       )
         __lowerCamelCase            =    Text('''CPU'''    ,							font_size=24       )
         __lowerCamelCase            =    Group(a    ,							a       ).arrange(a    ,							buff=0.5    ,							aligned_edge=a       )
         cpu.move_to([-2.5, -0.5, 0]       )
         self.add(a       )

         __lowerCamelCase            =    [mem.copy() for i in range(4       )]
         __lowerCamelCase            =    VGroup(*a       ).arrange(a    ,							buff=0       )
         __lowerCamelCase            =    Text('''GPU'''    ,							font_size=24       )
         __lowerCamelCase            =    Group(a    ,							a       ).arrange(a    ,							buff=0.5    ,							aligned_edge=a       )
         gpu.move_to([-1, -1, 0]       )
         self.add(a       )

         __lowerCamelCase            =    [mem.copy() for i in range(6       )]
         __lowerCamelCase            =    VGroup(*a       ).arrange(a    ,							buff=0       )

         __lowerCamelCase            =    Text('''Model'''    ,							font_size=24       )
         __lowerCamelCase            =    Group(a    ,							a       ).arrange(a    ,							buff=0.5    ,							aligned_edge=a       )
         model.move_to([3, -1.0, 0]       )
         self.add(a       )

         __lowerCamelCase            =    []
         __lowerCamelCase            =    []

         for i, rect in enumerate(a       ):
             __lowerCamelCase            =    fill.copy().set_fill(a    ,							opacity=0.8       )
             target.move_to(a       )
             model_arr.append(a       )

             __lowerCamelCase            =    Rectangle(height=0.46    ,							width=0.46       ).set_stroke(width=0.0       ).set_fill(a    ,							opacity=0.8       )
             cpu_target.move_to(cpu_left_col_base[i]       )
             model_cpu_arr.append(a       )

         self.add(*a    ,							*a       )

         __lowerCamelCase            =    [meta_mem.copy() for i in range(6       )]
         __lowerCamelCase            =    [meta_mem.copy() for i in range(6       )]
         __lowerCamelCase            =    VGroup(*a       ).arrange(a    ,							buff=0       )
         __lowerCamelCase            =    VGroup(*a       ).arrange(a    ,							buff=0       )
         __lowerCamelCase            =    VGroup(a    ,							a       ).arrange(a    ,							buff=0       )
         __lowerCamelCase            =    Text('''Disk'''    ,							font_size=24       )
         __lowerCamelCase            =    Group(a    ,							a       ).arrange(a    ,							buff=0.5    ,							aligned_edge=a       )
         disk.move_to([-4, -1.25, 0]       )
         self.add(a    ,							a       )

         __lowerCamelCase            =    Square(side_length=2.2       )
         key.move_to([-5, 2, 0]       )

         __lowerCamelCase            =    MarkupText(
             f"""<b>Key:</b>\n\n<span fgcolor='{YELLOW}'>●</span> Empty Model"""    ,							font_size=18    ,							)

         key_text.move_to([-5, 2.4, 0]       )

         self.add(a    ,							a       )

         __lowerCamelCase            =    MarkupText(
             f"""<span fgcolor='{BLUE}'>●</span> Checkpoint"""    ,							font_size=18    ,							)

         blue_text.next_to(a    ,							DOWN * 2.4    ,							aligned_edge=key_text.get_left()       )
         self.add(a       )

         __lowerCamelCase            =    MarkupText(
             f"""Now watch as an input is passed through the model\nand how the memory is utilized and handled."""    ,							font_size=24    ,							)
         step_a.move_to([2, 2, 0]       )

         self.play(Write(a       )       )

         __lowerCamelCase            =    Square(0.3       )
         input.set_fill(a    ,							opacity=1.0       )
         input.set_stroke(width=0.0       )
         input.next_to(model_base[0]    ,							a    ,							buff=0.5       )

         self.play(Write(a       )       )

         input.generate_target()
         input.target.next_to(model_arr[0]    ,							direction=a    ,							buff=0.02       )
         self.play(MoveToTarget(a       )       )

         self.play(FadeOut(a       )       )

         __lowerCamelCase            =    Arrow(start=a    ,							end=a    ,							color=a    ,							buff=0.5       )
         a.next_to(model_arr[0].get_left()    ,							a    ,							buff=0.2       )

         model_cpu_arr[0].generate_target()
         model_cpu_arr[0].target.move_to(gpu_rect[0]       )

         __lowerCamelCase            =    MarkupText(
             f"""As the input reaches a layer, the hook triggers\nand weights are moved from the CPU\nto the GPU and back."""    ,							font_size=24    ,							)
         step_a.move_to([2, 2, 0]       )

         self.play(Write(a    ,							run_time=3       )       )

         __lowerCamelCase            =    {'''run_time''': 1, '''fade_in''': True, '''fade_out''': True, '''buff''': 0.02}

         self.play(
             Write(a       )    ,							Circumscribe(model_arr[0]    ,							color=a    ,							**a       )    ,							Circumscribe(model_cpu_arr[0]    ,							color=a    ,							**a       )    ,							Circumscribe(gpu_rect[0]    ,							color=a    ,							**a       )    ,							)
         self.play(MoveToTarget(model_cpu_arr[0]       )       )

         __lowerCamelCase            =    a.copy()
         for i in range(6       ):
             a_c.next_to(model_arr[i].get_right() + 0.02    ,							a    ,							buff=0.2       )

             input.generate_target()
             input.target.move_to(model_arr[i].get_right() + 0.02       )

             __lowerCamelCase            =    AnimationGroup(
                 FadeOut(a    ,							run_time=0.5       )    ,							MoveToTarget(a    ,							run_time=0.5       )    ,							FadeIn(a    ,							run_time=0.5       )    ,							lag_ratio=0.2       )

             self.play(a       )

             model_cpu_arr[i].generate_target()
             model_cpu_arr[i].target.move_to(cpu_left_col_base[i]       )

             if i < 5:
                 model_cpu_arr[i + 1].generate_target()
                 model_cpu_arr[i + 1].target.move_to(gpu_rect[0]       )
                 if i >= 1:
                     __lowerCamelCase            =    0.7

                 self.play(
                     Circumscribe(model_arr[i]    ,							**a       )    ,							Circumscribe(cpu_left_col_base[i]    ,							**a       )    ,							Circumscribe(cpu_left_col_base[i + 1]    ,							color=a    ,							**a       )    ,							Circumscribe(gpu_rect[0]    ,							color=a    ,							**a       )    ,							Circumscribe(model_arr[i + 1]    ,							color=a    ,							**a       )    ,							)
                 if i < 1:
                     self.play(
                         MoveToTarget(model_cpu_arr[i]       )    ,							MoveToTarget(model_cpu_arr[i + 1]       )    ,							)
                 else:
                     self.play(
                         MoveToTarget(model_cpu_arr[i]    ,							run_time=0.7       )    ,							MoveToTarget(model_cpu_arr[i + 1]    ,							run_time=0.7       )    ,							)
             else:
                 model_cpu_arr[i].generate_target()
                 model_cpu_arr[i].target.move_to(cpu_left_col_base[-1]       )
                 input.generate_target()
                 input.target.next_to(model_arr[-1].get_right()    ,							RIGHT + 0.02    ,							buff=0.2       )

                 self.play(
                     Circumscribe(model_arr[-1]    ,							color=a    ,							**a       )    ,							Circumscribe(cpu_left_col_base[-1]    ,							color=a    ,							**a       )    ,							Circumscribe(gpu_rect[0]    ,							color=a    ,							**a       )    ,							)

                 self.play(MoveToTarget(model_cpu_arr[i]       )       )

             __lowerCamelCase            =    a_c
             __lowerCamelCase            =    a_c.copy()

         input.generate_target()
         input.target.next_to(model_base[-1]    ,							RIGHT + 0.02    ,							buff=0.5       )
         self.play(
             FadeOut(a       )    ,							FadeOut(a    ,							run_time=0.5       )    ,							)

         __lowerCamelCase            =    MarkupText(f"""Inference on a model too large for GPU memory\nis successfully completed."""    ,							font_size=24       )
         step_a.move_to([2, 2, 0]       )

         self.play(Write(a    ,							run_time=3       )    ,							MoveToTarget(a       )       )

         self.wait()
